Discovery of 3-Formyl-Tyrosine Metabolites from Pseudoalteromonas tunicata through Heterologous Expression

Genome mining and identification of natural product gene clusters typically relies on the presence of canonical nonribosomal polypeptide synthetase (NRPS) or polyketide synthase (PKS) domains. Recently, other condensation enzymes, such as the ATP-grasp ligases, have been recognized as important players in natural product biosynthesis. In this study, sequence based searching for homologues of DdaF, the ATP-grasp amide ligase from dapdiamide biosynthesis, led to the identification of a previously unannotated biosynthetic gene cluster in Pseudoalteromonas tunicata. Heterologous expression of the cluster in Escherichia coli allowed for the production and structure determination of two new 3-formyl tyrosine metabolites.Molecular and Cellular Biolog

Dapdiamides, Tripeptide Antibiotics Formed by Unconventional Amide Ligases†

Construction of a genomic DNA library from Pantoea agglomerans strain CU0119 and screening against the plant pathogen Erwinia amylovora yielded a new family of antibiotics, dapdiamides A-E (1-5). The structures were established through 2D-NMR experiments and mass spectrometry, as well as the synthesis of dapdiamide A (1). Transposon mutagenesis of the active cosmid allowed identification of the biosynthetic gene cluster. The dapdiamide family's promiscuous biosynthetic pathway contains two unconventional amide ligases that are predicted to couple its constituent monomers

RNA-seq Analysis Reveals That an ECF σ Factor, AcsS, Regulates Achromobactin Biosynthesis in Pseudomonas syringae pv. syringae B728a

Iron is an essential micronutrient for Pseudomonas syringae pv. syringae strain B728a and many other microorganisms; therefore, B728a has evolved methods of iron acquirement including the use of iron-chelating siderophores. In this study an extracytoplasmic function (ECF) sigma factor, AcsS, encoded within the achromobactin gene cluster is shown to be a major regulator of genes involved in the biosynthesis and secretion of this siderophore. However, production of achromobactin was not completely abrogated in the deletion mutant, implying that other regulators may be involved such as PvdS, the sigma factor that regulates pyoverdine biosynthesis. RNA-seq analysis identified 287 genes that are differentially expressed between the AcsS deletion mutant and the wild type strain. These genes are involved in iron response, secretion, extracellular polysaccharide production, and cell motility. Thus, the transcriptome analysis supports a role for AcsS in the regulation of achromobactin production and the potential activity of both AcsS and achromobactin in the plant-associated lifestyle of strain B728a

MIBiG 3.0: a community-driven effort to annotate experimentally validated biosynthetic gene clusters

Microbial Biotechnolog

MIBiG 3.0 : a community-driven effort to annotate experimentally validated biosynthetic gene clusters

With an ever-increasing amount of (meta)genomic data being deposited in sequence databases, (meta)genome mining for natural product biosynthetic pathways occupies a critical role in the discovery of novel pharmaceutical drugs, crop protection agents and biomaterials. The genes that encode these pathways are often organised into biosynthetic gene clusters (BGCs). In 2015, we defined the Minimum Information about a Biosynthetic Gene cluster (MIBiG): a standardised data format that describes the minimally required information to uniquely characterise a BGC. We simultaneously constructed an accompanying online database of BGCs, which has since been widely used by the community as a reference dataset for BGCs and was expanded to 2021 entries in 2019 (MIBiG 2.0). Here, we describe MIBiG 3.0, a database update comprising large-scale validation and re-annotation of existing entries and 661 new entries. Particular attention was paid to the annotation of compound structures and biological activities, as well as protein domain selectivities. Together, these new features keep the database up-to-date, and will provide new opportunities for the scientific community to use its freely available data, e.g. for the training of new machine learning models to predict sequence-structure-function relationships for diverse natural products. MIBiG 3.0 is accessible online at https://mibig.secondarymetabolites.org/